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Basic Data Analysis for Time Series with R by DeWayne Derryberry

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INDEX

 

Note: Locators followed by “f ” and “t” refer to figures and tables respectively.

  • acf(),
    • autocovariance estimation coding
    • background
    • and spectrum
    • for white noise errors
  • acos()
  • AIC. See Akaike's information criteria
  • Akaike's information criteria (AIC)
    • as cross-validation, NYC temperatures
    • model selection with
  • anova()
  • arima.sim()
  • ARMA(2,2) model
  • AR(m) filtering matrix
    • filtering information
    • linear algebra
    • and lm()
    • to model MA(3)
    • standard computations
  • AR(1) model for irregular spacing
    • final analysis
    • method
    • motivation
    • results
    • sensitivity analysis
  • AR(m) structure, residuals for
    • data display
    • filtering twice
  • ar.yw()
  • asin()
  • Assumptions
    • equal variance
      • regression
      • two- sample t-test
    • independence
    • introduction
    • logarithmic transformations, illustration of
    • normality
      • heavy tails
      • left skew
      • right skewed
  • atan()
  • Autocorrelation
    • AR(1)
    • AR(2)
    • estimation
    • for MA(1) models
    • for MA(2) models
    • stationarity
  • Autocovariance
    • AR(1)
    • AR(2)
    • ARMA(m,l) model
    • estimation, 37
    • properties
    • stationarity
    • white noise
  • Autoregressive model of order 1, AR(1)
    • adjustments
      • implications
      • skip method
    • autocorrelation
    • autocovariance
    • definition
    • examples (stable and unstable models)
    • illustration
  • Autoregressive model of order 2, AR(2)
    • autocorrelation
    • autocovariance
    • examples 46t
    • and power spectrum
    • preliminary facts
    • R code
    • simulating data

 

  • Backshift operator
    • and ARMA(m,l) models
    • definition
    • examples
    • stationary condition for AR(1) model
  • Bayesian information criteria (BIC)
  • Best linear unbiased ...

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